Previous, current, and future stereotactic EEG techniques for localising epileptic foci

ABSTRACT

Introduction

Drug-resistant focal epilepsy presents a significant morbidity burden globally, and epilepsy surgery has been shown to be an effective treatment modality. Therefore, accurate identification of the epileptogenic zone for surgery is crucial, and in those with unclear noninvasive data, stereoencephalography is required.

Areas covered

This review covers the history and current practices in the field of intracranial EEG, particularly analyzing how stereotactic image-guidance, robot-assisted navigation, and improved imaging techniques have increased the accuracy, scope, and use of SEEG globally.

Expert Opinion

We provide a perspective on the future directions in the field, reviewing improvements in predicting electrode bending, image acquisition, machine learning and artificial intelligence, advances in surgical planning and visualization software and hardware. We also see the development of EEG analysis tools based on machine learning algorithms that are likely to work synergistically with neurophysiology experts and improve the efficiency of EEG and SEEG analysis and 3D visualization. Improving computer-assisted planning to minimize manual input from the surgeon, and seamless integration into an ergonomic and adaptive operating theater, incorporating hybrid microscopes, virtual and augmented reality is likely to be a significant area of improvement in the near future.

KEYWORDS:

• Intracranial EEG
• stereo-EEG
• epilepsy surgery
• drug resistant focal epilepsy
• computer-assisted planning
• robot-assisted surgery
• neuronavigation
• surgical planning

Article highlights

• A review of the history, indications, and current practices in stereotactic EEG implantation.

• Demonstration of the computer assisted planning clinical decision support software (CDSS) that are used to significantly improve and streamline implantation planning.

• A review of the future directions of presurgical planning, visualization, and analysis of EEG data, as well as microscope technology, virtual and augmented reality, robotic-assisted navigation and implantation, machine learning and artificial intelligence.

Declarations of interest

John S Duncan and Debayan Dasgupta receive funding from the Wellcome Trust Innovation Program (218,380/Z/19/Z). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Reviewers disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Additional information

Funding

This work was partly funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative BW.mn.BRC10269).